COPD GWAS variant at 19q13.2 in relation with DNA methylation and gene expression.

Chronic obstructive pulmonary disease (COPD) is among the major health burdens in adults. While cigarette smoking is the leading risk factor, a growing number of genetic variations have been discovered to influence disease susceptibility. Epigenetic modifications may mediate the response of the genome to smoking and regulate gene expression. Chromosome 19q13.2 region is associated with both smoking and COPD, yet its functional role is unclear. Our study aimed to determine whether rs7937 (RAB4B, EGLN2), a top genetic variant in 19q13.2 region identified in genome-wide association studies of COPD, is associated with differential DNA methylation in blood (N = 1490) and gene expression in blood (N = 721) and lungs (N = 1087). We combined genetic and epigenetic data from the Rotterdam Study (RS) to perform the epigenome-wide association analysis of rs7937. Further, we used genetic and transcriptomic data from blood (RS) and from lung tissue (Lung expression quantitative trait loci mapping study), to perform the transcriptome-wide association study of rs7937. Rs7937 was significantly (FDR < 0.05) and consistently associated with differential DNA methylation in blood at 4 CpG sites in cis, independent of smoking. One methylation site (cg11298343-EGLN2) was also associated with COPD (P = 0.001). Additionally, rs7937 was associated with gene expression levels in blood in cis (EGLN2), 42% mediated through cg11298343, and in lung tissue, in cis and trans (NUMBL, EGLN2, DNMT3A, LOC101929709 and PAK2). Our results suggest that changes of DNA methylation and gene expression may be intermediate steps between genetic variants and COPD, but further causal studies in lung tissue should confirm this hypothesis.

Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression.

BACKGROUND: Epigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks. RESULTS: We perform a functional genomics analysis on four epigenetic clocks, including Hannum's blood predictor and Horvath's multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expression in trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells. CONCLUSIONS: The ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.

An interferon-related signature characterizes the whole blood transcriptome profile of insulin-resistant individuals-the CODAM study.

BACKGROUND: Worldwide, the prevalence of obesity and insulin resistance has grown dramatically. Gene expression profiling in blood represents a powerful means to explore disease pathogenesis, but the potential impact of inter-individual differences in a cell-type profile is not always taken into account. The objective of this project was to investigate the whole blood transcriptome profile of insulin-resistant as compared to insulin-sensitive individuals independent of inter-individual differences in white blood cell profile. RESULTS: We report a 3% higher relative amount of monocytes in the insulin-resistant individuals. Furthermore, independent of their white blood cell profile, insulin-resistant participants had (i) higher expression of interferon-stimulated genes and (ii) lower expression of genes involved in cellular differentiation and remodeling of the actin cytoskeleton. CONCLUSIONS: We present an approach to investigate the whole blood transcriptome of insulin-resistant individuals, independent of their DNA methylation-derived white blood cell profile. An interferon-related signature characterizes the whole blood transcriptome profile of the insulin-resistant individuals, independent of their white blood cell profile. The observed signature indicates increased systemic inflammation possibly due to an innate immune response and whole-body insulin resistance, which can be a cause or a consequence of insulin resistance. Altered gene expression in specific organs may be reflected in whole blood; hence, our results may reflect obesity and/or insulin resistance-related organ dysfunction in the insulin-resistant individuals.